Systems and methods for attenuation compensation in nuclear medicine imaging based on emission data

ABSTRACT

Systems and methods for attenuation compensation in nuclear medicine imaging based on emission data are provided. One method includes acquiring emission data at a plurality of energy windows for a person having administered thereto a radiopharmaceutical comprising at least one radioactive isotope. The method also includes performing a preliminary reconstruction of the acquired emission data to create one or more preliminary images of a peak energy window and a scatter energy window and determining a body outline of the person from at least one of the reconstructed preliminary image of the peak energy window or of the scatter energy window. The method further includes identifying a heart contour and segmenting at least the left lung. The method additionally includes defining an attenuation map based on the body outline and segmented left lung and reconstructing an image of a region of interest of the person using an iterative joint estimation reconstruction.

BACKGROUND OF THE INVENTION

The subject matter disclosed herein relates generally to nuclearmedicine imaging systems, and more particularly to single photonemission computed tomography (SPECT) imaging systems and compensatingfor emission attenuation in SPECT systems, especially in cardiacimaging, using emission data.

Different types of imaging techniques are known and used for medicaldiagnostic imaging. For example, diagnostic nuclear imaging, such asSPECT imaging, is used to study radionuclide distribution in a subject,such as a patient. Typically, one or more radiopharmaceuticals orradioisotopes are injected into the patient. Gamma camera detectorheads, typically including a collimator, are placed adjacent to asurface of the patient to capture and record emitted radiation tothereby acquire image data. Different configurations are known whereinthe gamma cameras may remain in a fixed location/orientation (e.g.,focused detector modules) relative to an object of interest during ascan or may be rotated about the patient. Image reconstructiontechniques, such as backprojection, may then be used to construct imagesof radiotracer uptake distribution within internal structures of thesubject based upon the acquired image or acquired data, such as listdata.

While such conventional systems may provide quality images with gooddiagnostic value, photon attenuation is a major physical factoraffecting the quality of reconstructed images in SPECT systems. Suchattenuation may occur, for example, due to tissues between the sourcesof emissions and the system detectors. However, in SPECT imaging, andspecifically in cardiology, it is important to obtain an accurateemission image (a three-dimensional 3D map of the radioisotopedistribution within the imaged patient) in the presence of attenuation(in large part due to Compton scattered radiation) caused by thepatient's body.

In cardiac imaging, photon attenuation accounts for up to 85% loss ofemitted photons from the myocardium area. Moreover, data inconsistencieswith models used in image reconstruction from a quantitative point ofview are also spatially variant (e.g., 70-85% error within myocardiumonly in some cases). Thus, known reconstruction methods requireknowledge of the attenuation map, for example, the 3D model of thepatient tissue in areas affecting the radiation arriving at thedetector. These methods currently mostly rely on direction transmissionmeasurements that may include a radioactive source that is oftenineffective or measurements from an x-ray computed-tomography (CT)system that are costly, as well as can add radiation dose to thepatient, additional imaging time, geometrical mis-registration andresolution differences. Models may be used to characterize theattenuation, although actual attenuation may differ substantially.Moreover, because of the high variability of patient sizes and shapes, a“patient standard” can yield a poor reconstruction result.

BRIEF DESCRIPTION OF THE INVENTION

In accordance with an embodiment, a method for image reconstruction isprovided. The method includes acquiring emission data at a plurality ofenergy windows for a person having administered thereto aradiopharmaceutical comprising at least one radioactive isotope, whereinthe energy windows comprise (i) at least a peak energy window centeredaround a peak emission of the isotope and (ii) at least one scatterenergy window at an energy range lower than the peak energy window. Themethod also includes performing a preliminary reconstruction of theacquired emission data to create one or more preliminary images of thepeak energy window and the scatter energy window and determining a bodyoutline of the person from at least one of the reconstructed preliminaryimage of the peak energy window or the reconstructed preliminary imageof the scatter energy window. The method farther includes identifying aheart contour of the person from the reconstructed preliminary image ofthe peak energy window and segmenting at least the left lung of theperson from the reconstructed preliminary image of the scatter energywindow using the identified heart contour as a landmark. The methodadditionally includes defining an attenuation map based on at least thedetermined body outline and the segmented left lung and reconstructingan image of a region of interest of the person using an iterative jointestimation reconstruction including updating the attenuation map and theimage of the peak energy window, wherein the joint estimationreconstruction comprises using data acquired in the plurality of energywindows.

In accordance with another embodiment, a nuclear medicine (NM) imagingsystem is provided that includes a gantry and a plurality of nuclearmedicine (NM) cameras coupled to the gantry and configured to acquireemission data at a plurality of energy windows for a person havingadministered thereto a radiopharmaceutical comprising at least oneradioactive isotope, wherein the energy windows comprise (i) at least apeak energy window centered around a peak emission of the isotope and(ii) at least one scatter energy window at an energy range lower thanthe peak energy window. The NM imaging system also includes an imagereconstruction module configured to (i) perform a preliminaryreconstruction of the acquired emission data to create one or morepreliminary images of the peak energy window and the scatter energywindow, (ii) determine a body outline of the person from at least one ofthe reconstructed preliminary image of the peak energy window or thereconstructed preliminary image of the scatter energy window, (iii)identify a heart contour of the person from the reconstructedpreliminary image of the peak energy window, (iv) segment at least theleft lung of the person from the reconstructed preliminary image of thescatter energy window using the identified heart contour as a landmark,(v) define an attenuation map based on at least the determined bodyoutline and the segmented left lung and (vi) reconstruct an image of aregion of interest of the person using an iterative joint estimationreconstruction including updating the attenuation map and the image ofthe peak energy window, wherein the joint estimation reconstructioncomprises using data acquired in the plurality of energy windows.

In accordance with yet another embodiment, a non-transitory computerreadable storage medium for performing image reconstruction using aprocessor is provided. The non-transitory computer readable storagemedium includes instructions to command the processor to acquireemission data at a plurality of energy windows for a person havingadministered thereto a radiopharmaceutical comprising at least oneradioactive isotope, wherein the energy windows comprise (i) at least apeak energy window centered around a peak emission of the isotope and(ii) at least one scatter energy window at an energy range lower thanthe peak energy window. The non-transitory computer readable storagemedium also includes instructions to command the processor to perform apreliminary reconstruction of the acquired emission data to create oneor more preliminary images of the peak energy window and the scatterenergy window and determine a body outline of the person from at leastone of the reconstructed preliminary image of the peak energy window orthe reconstructed preliminary image of the scatter energy window. Thenon-transitory computer readable storage medium also includesinstructions to command the processor to identify a heart contour of theperson from the reconstructed preliminary image of the peak energywindow, segment at least the left lung of the person from thereconstructed preliminary image of the scatter energy window using theidentified heart contour as a landmark and define an attenuation mapbased on at least the determined body outline and the segmented leftlung. The non-transitory computer readable storage medium furtherincludes instructions to command the processor to reconstruct an imageof a region of interest of the person using an iterative jointestimation reconstruction including updating the attenuation map and theimage of the peak energy window, wherein the joint estimationreconstruction comprises using data acquired in the plurality of energywindows.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a simplified block diagram of an exemplary imaging systemconstructed in accordance with various embodiments.

FIG. 2 is a diagram illustrating a detector configuration for theimaging system of FIG. 1 in accordance with one embodiment.

FIG. 3 is a diagram illustrating a detector configuration for theimaging system of FIG. 1 in accordance with another embodiment.

FIG. 4 is a diagram of a detector module formed in accordance with oneembodiment.

FIG. 5 is a diagram of a detector module formed in accordance withanother embodiment.

FIG. 6 is a diagram illustrating different emissions.

FIG. 7 is a block diagram of a process flow in accordance with variousembodiments.

FIG. 8 is a graph illustrating different energy levels in an energyresponse profile.

FIG. 9 is a flowchart of a method for attenuation compensation inaccordance with various embodiments.

FIG. 10 is a diagram illustrating additional views acquired by detectormodules in accordance with various embodiments.

FIG. 11 is a diagram illustrating persistence images used in accordancewith various embodiments.

FIG. 12 is a diagram illustrating voxel mapping used in accordance withvarious embodiments.

FIG. 13 is a diagram illustrating reconstructed images in accordancewith various embodiments.

DETAILED DESCRIPTION OF THE INVENTION

The foregoing summary, as well as the following detailed description ofvarious embodiments, will be better understood when read in conjunctionwith the appended drawings. To the extent that the figures illustratediagrams of the functional blocks of the various embodiments, thefunctional blocks are not necessarily indicative of the division betweenhardware circuitry. Thus, for example, one or more of the functionalblocks (e.g., processors or memories) may be implemented in a singlepiece of hardware (e.g., a general purpose signal processor or a blockof random access memory, hard disk, or the like) or multiple pieces ofhardware. Similarly, the programs may be stand-alone programs, may beincorporated as subroutines in an operating system, may be functions inan installed software package, and the like. It should be understoodthat the various embodiments are not limited to the arrangements andinstrumentality shown in the drawings.

Described herein are systems and methods to determine and compensate forattenuation within nuclear medicine imaging systems, in particular,single photon emission computer tomography (SPECT) imaging systems.Various embodiments use only emission data acquired by the SPECT systemto estimate and compensate for attenuation.

FIG. 1 is a block diagram of an exemplary nuclear medicine imagingsystem 20 constructed in accordance with various embodiments, which inthis embodiment is SPECT imaging system. The system 20 in one embodimentincludes an integrated gantry 22 that further includes a rotor 24oriented about a gantry central bore 26. The rotor 24 is configured tosupport one or more nuclear medicine (NM) cameras 28 and 30 (two areshown for illustration). In various embodiments the NM cameras 28 and 30may be, for example, general purpose gamma cameras or non-generalpurpose gamma cameras, such as focused pinhole gamma camera modulesconfigured for cardiac imaging. The NM cameras 28 and 30 may be formedfrom different types of suitable material, which may be directconversion materials or indirect conversion materials, which may bepixelated detectors or cameras. For example, in indirect conversionmaterial, the scintillator, which is typically made of a crystallinematerial, such as sodium iodide (NaI), converts the received gammaradiation to lower energy light energy (e.g., in an ultraviolet range).In these systems, photomultiplier tubes then receive this light andgenerate image data corresponding to photons impacting specific discretepicture element (pixel) regions. In direct conversion material, such ascadmium zinc telluride (CZT), the impinging photons are converteddirectly into electrical signals.

The rotor 24 is further configured to rotate axially about anexamination axis including a patient table 34 that may include a bedthat is slidingly coupled to a bed support system to support a patient36, which may be coupled directly to a floor or may be coupled to thegantry 22 through a base coupled to the gantry 22. The bed may include astretcher slidingly coupled to an upper surface of the bed. The patienttable 34 is configured to facilitate ingress and egress of the patient36 into an examination position that is substantially aligned with theexamination axis. During an imaging scan, the patient table 34 may becontrolled to move the bed and/or stretcher axially into and out of thebore 26. The operation and control of the imaging system 20 may beperformed in any manner known in the art. It should be noted that thevarious embodiments may be implemented in connection with imagingsystems that include rotating gantries or stationary gantries.

A collimator 38 may be provided in combination with the NM cameras 28and 30. For example, a collimator 38 may be coupled to front detectingfaces of each of the NM cameras 28 and 30. The collimators 38 may be anysuitable type of collimator known in the art.

The outputs from the NM cameras 28 and 30 are communicated to aprocessing unit 40, which may be any suitable computer or computingdevice. As used herein, the term “computer” or “module” may include anyprocessor-based or microprocessor-based system including systems usingmicrocontrollers, reduced instruction set computers (RISC), ASICs, logiccircuits, and any other circuit or processor capable of executing thefunctions described herein. The above examples are exemplary only, andare thus not intended to limit in any way the definition and/or meaningof the term “computer”.

The processing unit 40 may include an attenuation compensation module 50to perform attenuation compensation as described in more detail herein.The attenuation compensation module 50 may be implemented in hardware,software, or a combination of hardware and software.

It should be noted that the imaging system 20 may also be amulti-modality imaging system, such as an NM/MR imaging system. Duringan imaging scan, the patient table 34 may be controlled by a tablecontroller unit 44 that is part of a controller 42. The table controllerunit 44 may control the patient table 34 to move the patient table 34axially into and out of the bore 26. The NM cameras 28 and 30 may belocated at multiple positions (e.g., in an L-mode configuration) withrespect to the patient 36. It should be noted that although the NMcameras 28 and 30 are configured for movable operation along (or about)the gantry 22, the NM cameras 28 and 30 may be fixed thereto. Thecontroller 42 also includes a gantry motor controller 46 that controlsmovement of the gantry 22, for example, rotational movement about thepatient or movement of the NM cameras 28 and 30, such as pivotingmovement or movement towards/away from the patient 36.

Thus, the controller 42 may control the movement and positioning of thepatient table 34 with respect to the gamma cameras 28 and 30 and themovement and positioning of the NM cameras 28 and 30 with respect to thepatient 36 to position the desired anatomy (e.g., organ(s)) of thepatient 36 within the fields of view (FOVs) of the NM cameras 28 and 30,which may be performed prior to acquiring an image of the organ ofinterest. The table controller 44 and the gantry motor controller 46each may be automatically commanded by the processing unit 40, manuallycontrolled by an operator, or a combination thereof. The imaging datamay be combined and reconstructed into an image as described in moredetail below, which may comprise 2D images, a 3D volume or a 3D volumeover time (4D).

A Data Acquisition System (DAS) 48 receives analog and/or digitalelectrical signal data produced by the NM cameras 28 and 30 and decodesthe data for subsequent processing as described in more detail herein.An image reconstruction processor 52 receives the data from the DAS 48and reconstructs an image using any reconstruction process known in theart with attenuation compensation as described herein. A data storagedevice 54 may be provided to store data from the DAS 48 or reconstructedimage data. An input device 56 also may be provided to receive userinputs and a display 58 may be provided to display reconstructed images.

In operation, prior to data collection, a radioisotope, such as aradiopharmaceutical substance (sometimes referred to as a radiotracer),is administered to the patient 36, and may be bound or taken up byparticular tissues or organs. Typical radioisotopes include variousradioactive forms of elements, although many in SPECT imaging are basedupon an isotope of technetium (99Tc) that emits gamma radiation duringdecay. Various additional substances may be selectively combined withsuch radioisotopes to target specific areas or tissues of the body.

Gamma radiation emitted by the radioisotope, temporarily present at alocation within the patient is detected by the NM cameras 28 and 30.Although the NM cameras 28 and 30 are illustrated in FIG. 1 as planardevices positioned above the patient 36, the NM cameras 28 and 30 may bepositioned below the patient 36, both above and below the patient 36,next to the patient 36 and may wrap at least partially around thepatient 36.

The imaging system 20 in some embodiments may be coupled to one of morenetworks to allow for the transfer of system data to and from theimaging system 20, as well as to permit transmission and storage ofimage data and processed images. For example, a local area networks,wide area networks, wireless networks, and so forth may allow forstorage of image data on radiology department information systems or onhospital information systems. Such network connections further allow fortransmission of image data to remote post-processing systems, physicianoffices, and so forth.

The various embodiments described herein may be used, for example, inconjunction with dedicated SPECT systems for imaging particular organsof interest, such as for cardiac imaging and evaluation. Some of thesesystems are characterized by a limited field of view (FOV) aimed tocontain the organ of interest and/or non-parallel collimation. Suchsystems are sometime referred to as “shift variant” imaging systems.Here, shift variance means that system response to an object (e.g.,point source) differs depending on location of the object in the FOV.Among the differences are differences in geometrical shape of systemresponse, system sensitivity, and attenuation path from an emittingobject to the system detectors.

The various embodiments may be used in connection with different SPECTimaging configurations, such as shown in FIGS. 2 and 3. In theembodiment of FIG. 2, an imaging system scanner 60 comprises the gantry22 that supports a dual head camera (shown in an L-configuration). Thecamera comprises two camera sections, illustrated as the NM cameras 28and 30 disposed in the gantry 22 to acquire data over approximately 180degrees around the patient 36. In the embodiment illustrated, theimaging system scanner 60 is configured for cardiac imaging, and theembodiments described herein allow for characterization and correctionof scatter and attenuation of emissions 62 originating at locations inand around the heart 64. In general, such emissions will traverse atleast some regions of the heart 64, as well as soft tissues 66 of thebody, and particularly the left lung 68. It should be noted that thegamma cameras NM and 30 may be mounted to the gantry 22 with mountingmechanisms 70 that allow for movement in addition to about the patient36, such as pivoting movement or translation towards or away from thepatient 36.

FIG. 3 illustrates another configuration of an imaging scanner 80allowing that defines a multi-pinhole acquisition system that include aplurality of modules 82, which in this embodiment are pinhole gammacamera modules. The modules 82 are positioned and oriented around thepatient volume to collect emissions 62 that traverse similar tissues ofthe patient 36. It should be noted that in the case of pinholeacquisition systems, the pinholes of the modules 82 may be adjusted suchthat the pinholes are focused on the volume on interest and may bestationary during image acquisition, for example, the Discovery NM 530 cavailable from GE Healthcare.

It should also be noted that other types and configurations of camerasmay be employed, such as a camera of the type disclosed in U.S. Pat. No.6,242,743.

The modules 82 may take different forms as illustrated in FIGS. 4 and 5.For example, the pinhole configuration as illustrated in FIG. 4 includesa detector 90 having pinhole collimator 92 in combination therewith, forexample, coupled to a detecting face 94 of the detector 90. The module82 may pivot or rotate.

The module 82 may also include different types of collimation, such as aparallel hole collimator 96 as shown in FIG. 5. However, other types ofcollimation may be used including diverging and converging types ofcollimation as known in the art. In the embodiment of FIG. 5, a pivot 98is provided.

FIG. 6 illustrates a more detailed illustration of the tissues traversedby the radiation emissions in these scenarios. The body of the patient36 will extend to a skin-air boundary and have a general contour 100 inthe imaging volume from which SPECT data is acquired. Within the bodyand in the case of cardiac imaging, the heart 64 will have a contour 102that represents the boundary or transition between the tissues of theheart 64 and those of surrounding anatomies. The lung 68 (which is theleft lung) will have a further contour 104 representing the extent andthe transition between the lung tissues and those surrounding tissues.During SPECT imaging data acquisition, emissions 62 may radiate in alldirections and traverse some or all of these tissues and be scatteredand attenuated differently by each. For example, certain lines ofradiation 106 may traverse cardiac and soft tissues only, while otherlines of radiation 108 may traverse cardiac tissues and lung tissueswith little soft tissue therebetween, followed by soft tissue up to thebody contour. Still further lines of radiation 110 (direct radiation)may traverse cardiac tissues, soft tissues, and then further traversethe lung and more soft tissue before exiting the body. Some radiation112 may scatter as well, wherein an electron 114 in the tissue scattersthe gamma. The various embodiments use these contours for characterizingthe scatter and/or attenuation of the emissions for image dataprocessing and image reconstruction.

Various embodiments provide attenuation correction in SPECT using onlyemission data. A process flow 120 is shown in FIG. 7 that generallyillustrates attenuation compensation performed in some embodiments. Adetailed description will then follow. The process flow 120 includesperforming a preliminary reconstruction 122 based on acquired emissiondata. The acquired emission data that is used in the process flow 120includes a full spectrum of energies in various embodiments as shown inFIG. 8 to create at least preliminary images of the main emission orpeak energy window and the one or more scatter windows. Thus, emissiondata is acquired for a patient at a plurality of energy windows orlevels (e.g., list mode data) such that multiple energy windows can beretrospectively defined. For example, a peak energy window, illustratedas the main energy window 140 corresponding to the dominant energy peakin the energy response 144 and one or more scatter energy windows 142corresponding to lesser or no energy peaks in the energy response 144are acquired. In various embodiments, the main energy window isgenerally centered around the peak emission of the isotope and thescatter energy windows are at energy ranges lower than the peak energywindow. It should be noted the energy response 144 defines a profilethat may identify photons with small angle scatter and large anglescatter. Thus, scatter information, such as in lower energy windows thatdetect scatter with different scatter angles may be used in variousembodiments. It should be noted that scatter with a smaller scatterangle has a smaller deviation and smaller energy loss, while scatterwith a larger scatter angle has a larger deviations and larger energyloss.

Referring again to FIG. 7, this preliminary reconstruction 122 isperformed without correction for physical effects, namely no attenuationcorrection. This preliminary reconstruction 122 generally defines aboundary of interest, for example, the outer boundary of the patient. Itshould be noted that the preliminary reconstruction 122 may be performedusing only the main energy window or optionally include scatter datafrom one or more of the scatter energy windows (to improve outerboundary detection). Thus, a rough estimation 124 of body contours isdetermined, for example, by segmenting the body outline using areconstructed preliminary image of the peak energy window and optionallyone or more scatter energy windows.

An identification 126 of the heart contour of the patient is thendetermined using from the reconstructed preliminary image. Thisidentification 126 may be performed using any method known in the art. Asegmentation 128 of at least the left lung is then performed, which mayinclude using the identified heart contour to assist the lungsegmentation. For example, the boundary between the left ventricle andthe lung may be identified and then a seeding and growing process may beused to identify the boundary of the left lung. Thus, the left lung maybe segmented from the reconstructed preliminary image of the scatterenergy window using the identified heart contour as a landmark.

Binary maps generated from the rough estimation of the body contour andthe segmented left lung filled with linear attenuation coefficients arethen used as an input to a joint estimation reconstruction 130. Theinputs define an initial approximation or guess of the attenuation map,namely a preliminary attenuation map. The joint estimationreconstruction 130 is an iterative process wherein two updates areperformed at each iteration. First, an estimate of the attenuation mapis used to perform attenuation correction, which is then used to updatethe emission data. Thus, at each step, the emission estimate is updatedbased on the attenuation map from the previous iteration step, which isthen used to update the attenuation map in the current step. The jointestimation reconstruction 130 is accordingly performed with attenuationand scatter compensation to generate a reconstructed image 132.

Thus, in various embodiments, the body outline is identified using thepeak energy window, the scatter energy window or a combination thereof,for example, a summation of the peak energy window and the scatterenergy window. The heart contour is identified from the reconstruction“peak”. The lung(s) are identified from scatter data, such as using oneor more scatter energy windows. Various operations or steps to identifythe different landmarks and compensate for attenuation may be performed,for example, as described in more detail below.

More particularly, various embodiments provide a method 150 as shown inFIG. 9 for attenuation compensation, particularly in SPECT imaging,especially cardiac SPECT imaging. The method 150 includes acquiringemission data for multiple energy windows at 152, which is used tosegment a body outline with a preliminary reconstruction at 154. Thus,using emission data for multiple energy windows (peak energywindow+scatter energy window(s)), a preliminary reconstruction withoutattenuation correction is performed. In one embodiment, a main emissionor peak energy window reconstruction using any suitable SPECTreconstruction method may be used to determine a rough estimation of thebody contour. As described in more detail herein, scatter energy windowreconstruction may be used to supplement the main emission or peakenergy window reconstruction. Thus, no x-ray CT data is used in themethod 150.

In some embodiments, additional projection views are acquired at 152.For example, additional projection views are acquired from the supinedirection to a standard 180 degree acquisition arc as shown in FIG. 10,such as using rotational SPECT. Thus, additional views may be acquiredto resolve the body outline by rotating the NM cameras or detectors(e.g., the NM cameras 28 and 30) additional gantry steps, for example,which in one embodiment is a distance about equal to the size of the NMcamera or detector. For example, FIG. 10 illustrates three gantrypositioned for the NM cameras 28 and 30. It should be noted that the NMcameras 28 and 30 are rotated through a plurality of gantry steps andonly three are shown for illustration. As can be seen, locations 1 and 2are part of the standard 180 degree acquisition while location 3acquires additional views.

In some embodiments, for example in a focused collimation system 170(having a focused geometry) as shown in FIG. 11, persistence data 172may be used. The persistence data 172 is acquired, for example, duringpositioning of the patient and is not discarded in these embodiments.Thus, this persistence data 172 provides data similar to that of a scoutdata.

Referring again to FIG. 9, thereafter the heart contour is segmented at156 from the reconstructed preliminary image of the peak energy windowand optionally one or more scatter energy windows. The identification ofthe heart may be determined using any suitable known heart segmentationmethod. The heart contour optionally may be used as a landmark todetermine an interface between the heart and the left lung to assist inlung segmentation at 158. For example, the heart contour may be enclosedin an ellipsoid (e.g., graphical overlay) with the left lung identifiedat an interface using a known direction from the left ventricle of theheart.

Thus, at 158, the left lung is segmented using a scatter windowreconstruction, namely from the reconstructed preliminary image of thescatter energy window(s). For example, using scatter data, a scatterwindow reconstruction may be performed using a regular reconstruction,such as a main window reconstruction with straight line projections fromthe emission to the detector such that geometry changes are ignored. Insome embodiments, a special projector for scatter reconstruction may beused such as a model, for example, a Monte-Carlo based method to modelthe scatter geometry to improve lung contrast. It should be noted thatall or a subset of the voxels may be updated at a time.

Thus, a segmentation based determination of the lung may be used toobtain a binary map by using the voxel values and predeterminedthreshold values, such as to identify tissue. In some embodiments, thesegmentation may be assisted by a knowledge set such as an a prioriconstructed lung model.

Then, an iterative joint estimation reconstruction using data from thedetermined contours is performed at 160 wherein each iteration includestwo updates. In various embodiments, a preliminary attenuation map isdefined based on the determined body outline and the segmented leftlung. In particular, in each iteration the emission data isreconstructed with an attenuation correction estimate and theattenuation map is updated. For example, a maximum likelihood processmay be used for the emission update and a conjugate gradient-likeprocess may be used for the attenuation map update. It should be notedthat in some embodiments specifically constructed priors (e.g., addingregularizations), such as joint entropy or other intermediate filters(based on neighbor voxels), or bi-normal distribution may be used toprovide smoothness to the resulting images and form the images todevelop desired properties.

Thus, various embodiments provide a reconstruction process in two mainstages. First, an initial estimate of the attenuation map is createdfrom a series of reconstruction and segmentation steps. Second, thisestimate, along with SPECT emission projections, are used as an input(initial approximation) for an iterative joint estimation process, whenSPECT data reconstruction with attenuation and, optionally, scattercompensation, and attenuation map estimate are interchangeably updatedand refined until a pre-defined criterion is met. It should be notedthat the various steps of the method 150 may be achieved in a singlestep, by reconstructing an optimized scatter window.

Reconstruction with attenuation and scatter compensation may assign avalue to the scatter/attenuation for different trajectories through thetissues traversed by each trajectory. For example, FIG. 12 is adiagrammatical illustration of such mapping in the case of cardiacimaging. In this illustration, the mapping 180 is compiled for anatomiesof interest and shown disposed in a three-dimensional segment consistingof discrete volume elements or voxels 182. Based upon the density andposition of the various tissues, the voxels may indicate more or lessscatter/attenuation. The mapping may be determined from the body andtissue contour and volume determinations as described in more detailherein, and used in the reconstruction of images from the acquired SPECTdata.

In operation, and for example, the rough estimation of the body contourmay be performed in various ways, depending on geometry of acquisition.For conventional rotational SPECT acquisitions, reconstruction of countsin the main emission window, and/or counts detected in the scatterwindow is used. For alternative geometries, such as characterized bystationary acquisition/limited acquisition arc/small field of view(FOV), additional data, such as scout or persistence data from remotedetector positions may be added. In this case, several auxiliary viewsmay be appended to the projection data corresponding to standardacquisition orbit/geometry and reconstructed together, to ensure fullvisibility of the body outline as described herein. This initialreconstruction will be then segmented into “body” and “outside air”classifications.

Following this body contour estimation phase, and in the case of cardiacimaging, a rough estimation of the left lung volume is performed. Thisestimation may be based upon “seeding” from the edge of thereconstructed left ventricular surface to provide an additional landmarkfor lung identification and segmentation. The data resulting from thisphase defines an initial estimation of the attenuation maps. Theattenuation map is reconstructed on the same voxel grid and volume asthe emission data, and from the same data. Thus, the attenuation mapsobtained from this process are intrinsically registered to the emissiondata.

For example, as illustrated in FIG. 13, a preliminary reconstruction ofthe acquired emission data may be performed to create at leastpreliminary images 190 and 192 of the peak energy window and scatterenergy window, respectively. The heart may be identified as shown in theimage 194, including providing an overlay 195 such that the leftventricle contour of the heart is segmented. The arrows in image 194represent lung search “seeds” to segment the left lung 198 asillustrated in the image 196.

Thus, with the attenuation map determined, image reconstruction withcompensation for scatter/attenuation of SPECT emissions may beperformed. In various embodiments, the determination of the contours asshown, for example, in FIG. 13, provides the initial estimate of theattenuation map, which will be enhanced further in the process of jointestimation as described herein.

In operation, the second stage of the reconstruction process results infinal SPECT images reconstructed with compensation for effects ofattenuation and optionally scatter. In accordance with one embodiment,the reconstruction process is iterative, and may be provided asdescribed below. As an initialization step, activity uptake distributionis assumed, in a standard manner, to be uniform in accordance with therelationship:X^((0)=M) ^((0)=H′)1_(p)  Eq. 1wherein SPECT emission projections, initial estimate of attenuation mapand, optionally, scatter estimate (same volume as the volume of emissionreconstruction) are the inputs.

Following initialization, joint estimation is performed. For eachiteration of the joint estimation process, two subsequent updates areperformed. First, activity concentration estimate x (SPECTreconstruction) is advanced following, for example, a conventionalpenalized likelihood scheme. In this update, a current estimate of theattenuation map x^(k) is used. In the second update, the attenuation mapestimate is refined using the just obtained activity concentrationestimate. The update of the attenuation map, which does not obey Poissonstatistics, is not driven by likelihood maximization, but by a generaloptimization scheme such as a coordinate descent. So, a single iterationof a joint estimation algorithm may be described by the relationships:

$\begin{matrix}{x^{({k + 1})} = {\underset{x}{\arg\;\min}\left( {{L\left( {g,x} \right)} + {\beta\;{P\left( {x,m^{(k)}} \right)}}} \right.}} & {{Eq}.\mspace{14mu} 2} \\{m^{({k + 1})} = {\underset{m}{\arg\;\min}\left( {F\left( {x^{({k + 1})},m} \right)} \right)}} & {{Eq}.\mspace{14mu} 3}\end{matrix}$where x represents the activity distribution and in the attenuation map.

From Equation 2, the value of x is updated in accordance with therelationship:

$\begin{matrix}{x_{j}^{({k + 1})} = {\frac{x_{j}^{(k)}}{{\sum\limits_{i}h_{ij}} + {\beta\frac{\mathbb{d}{P\left( {x^{(k)},m^{(k)}} \right)}}{\mathbb{d}x}}}{\sum\limits_{i}{h_{ij}\frac{g_{i}}{\sum\limits_{j^{\prime}}{h_{{ij}^{\prime}}x_{j^{\prime}}^{(k)}}}}}}} & {{Eq}.\mspace{14mu} 4}\end{matrix}$while a numerical, coordinate descent like framework is applied toadvance m. In the process of joint reconstruction, the attenuation mapestimate is maintained smooth in various embodiments. Among other waysto ensure this smoothness is a cross-entropy based prior can beutilized, where:

$\begin{matrix}{{{P_{j}\left( {m,\hat{m}} \right)} = {\sum\limits_{n^{\prime} \in N_{j}}{w_{j,n^{\prime}}\left( {{m_{j}\ln\frac{m_{j}}{{\hat{m}}_{n^{\prime}}}} - m_{j} + {\hat{m}}_{n^{\prime}}} \right)}}}{\frac{\mathbb{d}{P\left( {m,\hat{m}} \right)}}{\mathbb{d}x} = {\sum\limits_{n^{\prime} \in N_{j}}{w_{j,n^{\prime}}\left( {{\ln\frac{m_{j}}{{\hat{m}}_{n^{\prime}}}} - 1} \right)}}}{{\hat{m}}_{j}^{(k)} = \frac{\sum\limits_{n^{\prime} \in N_{j}}{w_{j,n^{\prime}}m_{j}^{(k)}}}{\sum\limits_{n^{\prime} \in N_{j}}w_{j,n^{\prime}}}}} & {{Eq}.\mspace{14mu} 5}\end{matrix}$where m is the attenuation map estimate and {circumflex over (m)} issome auxiliary image. Under a cross-entropy framework, each voxel of{circumflex over (m)} is composed of weighted arithmetic means of itsneighboring voxels, imposing smoothness. An edge-preserving constraintmay be further implemented.

The values of m^((k)) are then thresholded in accordance with therelationship single threshold:

$\begin{matrix}{m_{j}^{({k + 1})} = \left\{ \begin{matrix}{{m_{j}^{({k + 1})} = \overset{\Cap}{c}};{m_{j}^{({k + 1})} \geq t^{({k + 1})}}} \\{{m_{j}^{({k + 1})} = \overset{\Cup}{c}};{else}}\end{matrix} \right.} & {{Eq}.\mspace{14mu} 6}\end{matrix}$(or using other segmentation techniques described known in the art) intoair and soft tissue compartments and filled with linear attenuationcoefficients from a look-up table, in accordance with theradiopharmaceutical used in the process of data formation.

Combining the stage of initial independent reconstruction of attenuationmap with joint estimation, “cross-talk” is reduced or eliminated.“Cross-talk” appears when emission-specific features (e.g., myocardialuptake) are being propagated into the attenuation map.

Variations and modifications are contemplated. For example, in oneembodiment the following process may be provided:

A. Preparation:

-   -   1. Data is to be collected in at least two energy windows (E0        and E1).    -   E0 is the “peak energy window”, defined as “peak energy+/−dE        wherein dE is usually a few percent;    -   E1 is energy window within the scatter energy range wherein the        energy range of the “scatter energy window” is below the “peak        energy window”, and in various embodiments is wider than the        “peak energy window”;    -   Optionally more scatter energy windows are defined such as E2        (or more), that is, the “scatter energy window” is divided to        two or more sub-windows, which in some embodiments are        non-overlapping, contiguous sub-windows. 2. Defining the system        response function—a function that allows estimating the data        provided that the object is known. The system response depends        on the collimator, the detector, etc.        B. Data Acquisition:    -   Collecting emission data “e(P,E)”, the number of photons        collected at detector position “P” at energy window “E”.    -   Here, “E” are the energy windows (E=0, 1, 2, . . . )    -   and “P” is the general designation of the detector position.        -   1. In a multi-pinhole camera P={x,y,p} wherein x, y are the            pixel indexes and p is the pinhole index.        -   2. In a rotating SPECT camera, P={x,y,p} wherein x, y are            the pixel indexes and p is the projection index (the gantry            angle “f”).        -   3. In a camera with a plurality of rotating, collimated            heads (such as in FIG. 5), P={x,y,p, f} wherein x, y are the            pixel indexes and p is the head index, and f is the angle of            head p. Optionally, f is defined by f={fx,fy} if the heads            can rotate in 2D.            It should be noted that the dimension of the dataset is            x*y*p*e (e=2 for two energy windows: “e0=peak”; and            “e1=scatter”, optionally e>2 if the scatter window is            subdivided). It also should be noted that the solution is of            the same (or lower) dimensionality.            C. Assumption and Definitions    -   It should be noted that a single isotope having a single        emission peak is assumed, (e.g. Tc having a peak at 140 keV),        but extension to multi-peak isotope or multi-isotope may be        provided.    -   1. The Object (patient)    -   The object O(X)={S,D}(X) is defined by both its:    -   “Source Concentration” S(x,y,z) S(X); (S is in “Curie per cc”,        x,y,z=X are the voxel index in 3D). Typically, the        dimensionality of x=y=z=64 or 128, the source concentration must        be a non-negative number    -   and    -   “body density” D(x,y,z)=D(X); (D in grams per cc). It should be        noted that D(x,y,z) may be translated to “Absorption & Compton        scattering coefficients” by the energy dependent nuclear        parameters cross section parameters. Typically, a linear        transformation is applied, but non-linear transformations (that        take into account bone chemical composition) are also known.        Tissue Segmentation.    -   To reduce the complexity of computation, the following algorithm        may be applied to D(X):    -   1. Air—For D˜0 (or below a threshold)—the tissue is assumed to        be “Air”. Specifically if it can be located as being outside the        patient boundaries. D is then set to D=0    -   2. Lungs—For D between “minimal lung density” and “Maximum lung        density”—The tissue a mixture of air and soft tissue, with the        required mixture percentage. Specifically if tissue can be        located as being inside the patient lungs boundaries. D is left        as a variable,    -   3. Soft tissue—For D between “maximal lung density” and “minimal        spongy bone density”—The tissue is assumed soft tissue. D is set        to the soft tissue average value    -   4. Spongy Bone—For D between “minimal spongy bone density” and        “maximal spongy bone density”—The tissue a mixture of soft        tissue and bone, with the required mixture percentage. D is left        as a variable and the chemical composition is assumed to be the        appropriate mixture of hard bone and soft tissue having density        D.    -   5. Hard bone—For D between “minimal hard bone density” and        “maximal hard bone density”—The tissue a hard bone. D is set to        density and hard bone.    -   6. Metal—For D above “maximal hard bone density”—The tissue an        implant or foreign object—operator intervention is required.    -   This process is called “segmentation of the patient tissue” (the        segments are: air, lungs, soft tissue, spongy bones, and hard        bone. However, in some cases, only air, lungs, and soft tissue        are considered and bones are ignored and replaced with soft        tissue or with “dense soft tissue having same artificially high        density).    -   Segmentation reduces the computation as most of the volume        comprises air or soft tissue.    -   The simple (linear) transformation from D to absorption and        scattering coefficients is:    -   Absorption A(X,E)=a(E)*D(X); wherein “a” is the average        absorption coefficient of tissue for energy E (ignoring minor        variation due to tissue types)    -   Compton scattering C(X,E,E′)=c(E,E′)*D(X) wherein c(E,E′) is the        average is the average Compton scattering coefficient of tissue        from energy E to energy E′ (which also define the scattering        angle, so in fact we could define C(X,E,t)=c(E,t)*D(X) wherein        “t” is the scattering angle)    -   Also note that the total attenuation U(X,E) is:    -   U(X,E)=A(X,E) Sum[{E′}, C(X,E,E′)] wherein Sum[{E′}, C(X,E,E′)]        is the summation for all E′ (or angles t) of C(X,E,E′).    -   2. The acquired data set    -   The emission data set is defined as e(P,E)={e0,e1,e2 . . . }(P)        wherein:    -   e0 is the number of photons detected having energy e0, etc.        (generally, e0 will be the un-scattered emission energy, while        e1,e2 . . . eN are the Compton scattered energies e1,e2 . . .        eN<e0.    -   P is generalized pixel number.    -   In a “stationary pinhole camera” P=(p,x,y) wherein p is the        pinhole number, x,y, are the pixel indexes associated with the        pixel.    -   In a SPECT (rotating) camera, P=(f,x,y) wherein f is the        projection angle    -   In “Spectrum Dynamic” camera, P=(h,f,x,y) wherein h is the head        number, f is the head′ angle.

Forward Projection—Estimation of “Peak” Data e0′(P) from S(X) and D(X)

-   -   Whatever the system is, P indicates the camera configuration.        Each P, is associated with a matrix element in the “response        function Matrix” M(P,X) which associate (gives the response, or        the sensitivity of the detector) a detected photon at        generalized pixel P with a radioactive source in location        (voxel) X in the body.    -   In a general sense, M(P,X) is the response function, define as        the probability of detecting a photon which was randomly emitted        from location X at generalized datum pixel P given the camera        geometry (including for example collimation, detector        sensitivity, etc. This may make M(P,X)=>M(P,X.E) for example        given energy dependence of septa penetration and/or detector        response), but excluding patient attenuation.    -   With absence of attenuation (D(X)=0), a source S(X) will        (statistically speaking—it is estimated to most likely to)        produce an acquired data e(P,E)={e0,e1,e2 . . .        }(P)={M(P,X)*S(X), 0, 0, . . . } or e0(P)=M(P,X)*S(X). Note that        M is very sparse (most of M elements are zero)    -   However, in presence of a real patient, the equation is modified        by attenuating each photon by the integrated (summed) total        attenuation U(X, e0) (as derived from D(X) along the traveling        line from the corresponding X to P.

Forward Scatter Estimation—Estimation of “Scattered” Data e1′(P) fromS(X) and D(X) (for Multi Scatter Windows—Also e2′(P), Etc)

-   -   There are several known methods disclosed in the art to        calculate e1′(P) when S′(X) and D′(X) are assumed known.    -   For example, “Monte Carlo simulations” may be used. Generally,        these are computational intensive (takes long time). Thus,        according to one embodiment, estimation of scattered data by        “accelerated Monte Carlo” may comprise the following        simplifications:    -   1. Define only one scatter window e1(P) at high energy range of        the scatter spectrum.    -   2. As a consequence:        -   A. Only scattering into small angles (“alpha max” which            produces less than the max energy loss) needs to be            computed, and        -   B. second order scattering can be ignored.        -   C. Attenuation coefficient of the scattered photon may be            taken as a single average value U(X,e1′), where e1′ is the            average energy of the photons in window e1.    -   The “Monte Carlo” simulation is accelerated if the following        steps are ignored:    -   1. For emitted photons:        -   A. ignore propagation direction in which there are no            possible valid scattering from them to the detector (taking            into account the limited “alpha max” and the collimator            acceptance).        -   B. If the path reaches pure air (out of the            patient)—terminate the photon.        -   C. Optionally ignore locations where S(X) us below a            threshold value.    -   2. Compute only the first scattering process (taking into        account D(X)): In this compute only:        -   A. Scattering a less than “alpha max”; and        -   B. Only into valid acceptance directions of the            collimator(s)    -   3. Adjust for absorption by (taking into account D′(X)):        -   A. U(X, e0) for the path from X0 to X1 (wherein X0 is the            origin of the photon and X1 is the location of the scatter            event); and        -   B. U(X, e1′) for the path from X1 to P (wherein X1 is the            location of the scatter event, and P is the detection            pixel); and

Thus, various embodiments find an accurate estimation S′(X) which is asclose as possible to the true source S(X) distribution.

For finding S′(X) an accurate estimation D′(X) which is as close aspossible to the true density D(X) is determined. D′(X) is used forattenuation correction of the emission image. D′(X) may be useful forthe operator for orienting S′(X) within the patient's body; and forability to register the image with anatomical images such as CT or MRI.

In some embodiments, a combined reconstruction algorithm may be providedthat includes:

-   -   1. measure data {e0(P), e1(P)}    -   2. start with initial guess {S′(X), D′(X)}    -   3. estimation of {e0′(P), e1′(P)} by forward projection of guess        {S′(X), D′(X)}    -   4. compare estimation {e0′(P), e1′(P)} to measured data {e0(P),        e1(P)}    -   5. update guess {S′(X), D′(X)} in view of #4    -   6. decision if to repeat steps #3 to #5, if not:    -   7. post processing and display the last updated guess {S′(X),        D′(X)}. (post processing may comprise filtering and image        analysis as known in the art)    -   8. stop

An operational alternative includes: Steps #3, #4, and #5 can berepeated a few times for S′(X) and e0′(P) only (which is more important,and less time consuming), then performing the steps for D′(X) ande1′(P). However, it should be noted that the combined problem is harderin several aspects:

-   -   1. The object is more complex: O(X)={S,D} (X)—there are two        unknown to find per “X”    -   2. The data set e(P) is more complex e(P)={e0, e1}(P)—there are        two measured value per “P”    -   3. Estimation of data has two parts:        -   a. Estimation of e0(P) as known in the art; and        -   b. Estimation of e1(P) as disclosed in the accelerated Monte            Carlo (above).    -   4. Updating the guess has two parts:        -   a. Updating the source S′(X) as known in the art (keeping            for example the following limitations: 1. positivity of            S(X), and 2. S(X)=0 outside the patient boundary); and        -   b. Updating the density D′(X) (keeping for example the            following limitations: 1. positivity of D′(X), 2. D′(X)=0            outside the patient boundary, 3. segmentation of D′(X) as            disclosed above).            -   According to one embodiment, updating the guess D′(X)                may comprise the following:                -   1. Subtract the estimated scattered data from                    measured scattered data to obtain error function:                    ERR(P)=e1(P)−e1′(P).                -   2. Reconstruct ERR(P) to produce a suggested change                    to guessed density deltaD′(X). Reconstruction may be                    done by methods known in the art. The reconstructed                    deltaD′(X) may be positive or negative, but it may                    limited by the following requirements:                -    Positivity of D′(X)—that is deltaD′(X) may not be                    larger than D′(X)                -    Limited range of D(X)—that is deltaD′(X)+D′(X) must                    not be larger than “density of hard bone”.                -    deltaD′(X)=0 outside the patient boundary                -   3. Update the guess of the density to be                    D′(X)=>D′(X)+deltaD′(X)                    Decision to stop (#6) may depend on the number of                    iterations and/or requiring a minimal match between                    both e0′ and e0 AND e1′ and e1                    Initial Approximation                    According to various embodiments:    -   1. Body boundary is calculated by reconstruction of e0(P)+e1(P)        (without attenuation correction), and defining the outer        perimeter outside which the reconstructed value is less than a        threshold value. Optionally a one or two “outward voxels        expansion” is performed to ensure that body parts are all        included within the boundary.    -   3. Initial guess for D′(X) may be taken as one of:        -   A. Defining all the volume within the body boundary            (calculated in #1) as “soft tissue”        -   B. Defining all the volume within the body boundary            (calculated in #1) as “soft tissue”, then introducing            “average lungs” by “morphing” lungs from “average patient            atlas”        -   C. Calculating D′(X) by reconstruction of e1(P) without            attenuation correction, but using the segmentation            limitation (disclosed above) and body boundary.    -   4. Initial guess for S′(X) is calculated by reconstruction of        e0(P) using methods of the art, using optionally:        -   A. Attenuation correction using D′(X) from above; and        -   B. Body boundaries from above.

A technical effect of various embodiments described herein includeattenuation compensation using only emission data.

It should be noted that the various embodiments may be implemented inhardware, software or a combination thereof. The various embodimentsand/or components, for example, the modules, or components andcontrollers therein, also may be implemented as part of one or morecomputers or processors. The computer or processor may include acomputing device, an input device, a display unit and an interface, forexample, for accessing the Internet. The computer or processor mayinclude a microprocessor. The microprocessor may be connected to acommunication bus. The computer or processor may also include a memory.The memory may include Random Access Memory (RAM) and Read Only Memory(ROM). The computer or processor further may include a storage device,which may be a hard disk drive or a removable storage drive, solid-statedrive, optical disk drive, and the like. The storage device may also beother similar means for loading computer programs or other instructionsinto the computer or processor.

As used herein, the term “computer” or “module” may include anyprocessor-based or microprocessor-based system including systems usingmicrocontrollers, reduced instruction set computers (RISC), ASICs, logiccircuits, and any other circuit or processor capable of executing thefunctions described herein. The above examples are exemplary only, andare thus not intended to limit in any way the definition and/or meaningof the term “computer”.

The computer or processor executes a set of instructions that are storedin one or more storage elements, in order to process input data. Thestorage elements may also store data or other information as desired orneeded. The storage element may be in the form of an information sourceor a physical memory element within a processing machine.

The set of instructions may include various commands that instruct thecomputer or processor as a processing machine to perform specificoperations such as the methods and processes of the various embodimentsof the invention. The set of instructions may be in the form of asoftware program. The software may be in various forms such as systemsoftware or application software and which may be embodied as a tangibleand non-transitory computer readable medium. Further, the software maybe in the form of a collection of separate programs or modules, aprogram module within a larger program or a portion of a program module.The software also may include modular programming in the form ofobject-oriented programming. The processing of input data by theprocessing machine may be in response to operator commands, or inresponse to results of previous processing, or in response to a requestmade by another processing machine.

As used herein, the terms “software” and “firmware” are interchangeable,and include any computer program stored in memory for execution by acomputer, including RAM memory, ROM memory, EPROM memory, EEPROM memory,and non-volatile RAM (NVRAM) memory. The above memory types areexemplary only, and are thus not limiting as to the types of memoryusable for storage of a computer program.

It is to be understood that the above description is intended to beillustrative, and not restrictive. For example, the above-describedembodiments (and/or aspects thereof) may be used in combination witheach other. In addition, many modifications may be made to adapt aparticular situation or material to the teachings of the variousembodiments without departing from their scope. While the dimensions andtypes of materials described herein are intended to define theparameters of the various embodiments, the embodiments are by no meanslimiting and are exemplary embodiments. Many other embodiments will beapparent to those of skill in the art upon reviewing the abovedescription. The scope of the various embodiments should, therefore, bedetermined with reference to the appended claims, along with the fullscope of equivalents to which such claims are entitled. In the appendedclaims, the terms “including” and “in which” are used as theplain-English equivalents of the respective terms “comprising” and“wherein.” Moreover, in the following claims, the terms “first,”“second,” and “third,” etc. are used merely as labels, and are notintended to impose numerical requirements on their objects. Further, thelimitations of the following claims are not written inmeans-plus-function format and are not intended to be interpreted basedon 35 U.S.C. §112, sixth paragraph, unless and until such claimlimitations expressly use the phrase “means for” followed by a statementof function void of further structure.

This written description uses examples to disclose the variousembodiments, including the best mode, and also to enable any personskilled in the art to practice the various embodiments, including makingand using any devices or systems and performing any incorporatedmethods. The patentable scope of the various embodiments is defined bythe claims, and may include other examples that occur to those skilledin the art. Such other examples are intended to be within the scope ofthe claims if the examples have structural elements that do not differfrom the literal language of the claims, or if the examples includeequivalent structural elements with insubstantial differences from theliteral languages of the claims.

What is claimed is:
 1. A method for image reconstruction, the methodcomprising: acquiring emission data at a plurality of energy windows fora person having administered thereto a radiopharmaceutical comprising atleast one radioactive isotope, wherein the energy windows comprise (i)at least a peak energy window centered around a peak emission of theisotope and (ii) at least one scatter energy window at an energy rangelower than the peak energy window; performing a preliminaryreconstruction of the acquired emission data to create one or morepreliminary images of the peak energy window and the scatter energywindow; determining a body outline of the person from at least one ofthe reconstructed preliminary image of the peak energy window or thereconstructed preliminary image of the scatter energy window;identifying a heart contour of the person from the reconstructedpreliminary image of the peak energy window; segmenting at least theleft lung of the person from the reconstructed preliminary image of thescatter energy window using the identified heart contour as a landmark;defining an attenuation map based on at least the determined bodyoutline and the segmented left lung; and reconstructing an image of aregion of interest of the person using an iterative joint estimationreconstruction including updating the attenuation map and the image ofthe peak energy window, wherein the joint estimation reconstructioncomprises using data acquired in the plurality of energy windows.
 2. Themethod of claim 1, wherein the preliminary reconstruction is performedwithout using x-ray computed tomography (CT) data.
 3. The method ofclaim 1, wherein acquiring the emission data comprises performing a 180degree single-photon emission computed tomography (SPECT) acquisitionscan arc.
 4. The method of claim 3, further comprising acquiringadditional views from a supine direction.
 5. The method of claim 3,further comprising using persistence data in the peak energy windowreconstruction to segment the body outline.
 6. The method of claim 1,wherein segmenting the left lung comprises scatter window reconstructionusing a Monte-Carlo based projection estimation method.
 7. The method ofclaim 1, wherein segmenting the left lung comprises using an a priorilung model.
 8. The method of claim 1, wherein the updating during theiterative joint estimation reconstruction comprises for each iterationusing a penalized likelihood maximization scheme to determine an updatedactivity distribution estimate for the peak energy window.
 9. The methodof claim 8, wherein the updating during the iterative joint estimationreconstruction comprises updating the attenuation map using the updatedactivity distribution estimate.
 10. The method of claim 8, wherein theupdating during the iterative joint estimation reconstruction comprisesupdating the attenuation map using a conjugate gradient method.
 11. Themethod of claim 1, wherein the updating during the iterative jointestimation reconstruction comprises using a constructed prior includingone of a joint entropy prior or a bi-normal distribution prior.
 12. Themethod of claim 1, further comprising reconstructing the attenuation mapon the same voxel grid and volume as the emission data.
 13. The methodof claim 1, wherein acquiring the emission data comprises acquiring thedata in at least a peak energy window and a plurality of scatter energywindows.
 14. The method of claim 13, further comprising segmentingtissue.
 15. The method of claim 1, further comprising performing aforward projection to determine scatter data using the same emissionprojections as the emission data.
 16. A nuclear medicine (NM) imagingsystem comprising: a gantry; a plurality of nuclear medicine (NM)cameras coupled to the gantry and configured to acquire emission data ata plurality of energy windows for a person having administered thereto aradiopharmaceutical comprising at least one radioactive isotope, whereinthe energy windows comprise (i) at least a peak energy window centeredaround a peak emission of the isotope and (ii) at least one scatterenergy window at an energy range lower than the peak energy window; andan image reconstruction module configured to (i) perform a preliminaryreconstruction of the acquired emission data to create one or morepreliminary images of the peak energy window and the scatter energywindow, (ii) determine a body outline of the person from at least one ofthe reconstructed preliminary image of the peak energy window or thereconstructed preliminary image of the scatter energy window, (iii)identify a heart contour of the person from the reconstructedpreliminary image of the peak energy window, (iv) segment at least theleft lung of the person from the reconstructed preliminary image of thescatter energy window using the identified heart contour as a landmark,(v) define an attenuation map based on at least the determined bodyoutline and the segmented left lung and (vi) reconstruct an image of aregion of interest of the person using an iterative joint estimationreconstruction including updating the attenuation map and the image ofthe peak energy window, wherein the joint estimation reconstructioncomprises using data acquired in the plurality of energy windows. 17.The NM imaging system of claim 16, wherein the image reconstructionmodule is configured to perform the preliminary reconstruction withoutusing x-ray computed tomography (CT) data.
 18. The NM imaging system ofclaim 16, wherein the plurality of NM cameras are configured to rotateabout the person to acquire the emission data from a 180 degreesingle-photon emission computed tomography (SPECT) acquisition scan arc.19. The NM imaging system of claim 16, wherein the plurality of NMcameras are configured to acquire additional views of the person from asupine direction.
 20. The NM imaging system of claim 16, wherein theimage reconstruction module is configured to use persistence dataacquired by the plurality of NM cameras in the peak energy windowreconstruction to segment the body outline.
 21. The NM imaging system ofclaim 16, wherein the plurality of NM cameras comprises focused detectormodules in a fixed orientation on the gantry.
 22. The NM imaging systemof claim 16, wherein the image reconstruction module is configured tosegment the left lung by at least one of (i) modeling scatter using aMonte-Carlo based estimation method or (ii) using an a priori lungmodel.
 23. The NM imaging system of claim 16, wherein the imagereconstruction module is configured to update at each iteration of theiterative joint estimation reconstruction the attenuation map using atleast one of (i) an updated activity distribution estimate determinedfrom the iterative joint estimation reconstruction wherein for eachiteration a penalized likelihood maximization scheme is used todetermine the updated activity distribution estimate for the peak energywindow or (ii) a conjugate gradient method.
 24. The NM imaging system ofclaim 16, wherein the image reconstruction module is configured to useduring the iterative joint estimation reconstruction a constructed priorincluding one of a joint entropy prior or a bi-normal distributionprior.
 25. The NM imaging system of claim 16, wherein the plurality ofNM cameras are configured to acquire the emission data in at least apeak energy window and a plurality of scatter energy windows.
 26. Anon-transitory computer readable storage medium for performing imagereconstruction using a processor, the non-transitory computer readablestorage medium including instructions to command the processor to:acquire emission data at a plurality of energy windows for a personhaving administered thereto a radiopharmaceutical comprising at leastone radioactive isotope, wherein the energy windows comprise (i) atleast a peak energy window centered around a peak emission of theisotope and (ii) at least one scatter energy window at an energy rangelower than the peak energy window; perform a preliminary reconstructionof the acquired emission data to create one or more preliminary imagesof the peak energy window and the scatter energy window; determine abody outline of the person from at least one of the reconstructedpreliminary image of the peak energy window or the reconstructedpreliminary image of the scatter energy window; identify a heart contourof the person from the reconstructed preliminary image of the peakenergy window; segment at least the left lung of the person from thereconstructed preliminary image of the scatter energy window using theidentified heart contour as a landmark; define an attenuation map basedon at least the determined body outline and the segmented left lung; andreconstruct an image of a region of interest of the person using aniterative joint estimation reconstruction including updating theattenuation map and the image of the peak energy window, wherein thejoint estimation reconstruction comprises using data acquired in theplurality of energy windows.